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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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评论 "使用基因组数据和机器学习来预测抗生素耐药性:一篇教程论文"

Davide Chicco1,2, Giuseppe Jurman3,4

  • 1Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy.

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概括
此摘要是机器生成的。

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科学领域:

  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习
  • 基因组学就是基因组学.

背景情况:

  • 最近的一项研究提出了一种机器学习课程,用于使用基因组学数据预测抗生素耐药性.
  • 该课程提供了对传统机器学习管道的逐步指南,可供初学者使用.

研究的目的:

  • 批评上述研究中建议的绩效评估方法.
  • 倡导使用马修斯相关系数 (MCC) 而不是准确性和F1分数来进行二进制分类.
  • 突出K倍交叉验证在机器学习模型开发中的潜在陷.

主要方法:

  • 发表的教学课程的正式评论和批评.
  • 对二进制分类表现的统计指标的解释.
  • 讨论机器学习中的数据分区技术.

主要成果:

  • 准确度和F1分数被认为是对二进制分类任务的误导性指标.
  • 马修斯相关系数 (MCC) 被提出作为一个更强大和可靠的指标.
  • 确定K倍交叉验证是一种具有重大缺陷和潜在陷的方法.

结论:

  • 在基因组学中评估二元分类模型时,应该优先考虑马修斯相关系数 (MCC).
  • 建议谨慎使用k倍交叉验证,因为它固有的局限性.
  • 准确的模型评估对于抗生素耐药性研究中的可靠预测至关重要.